The　high　dimensionality　of　hyperspectral　imagery　is　a　huge　challenge　for　remote　sensing　data　processing.　Band　selection　utilizes　the　most　distinctive　and　informative　band　subset　to　reduce　data　dimensions.　Although　band　selection　can　significantly　alleviate　the　computational　burden,　the　process　itself　may　be　time　consuming　because　it　needs　to　take　all　pixels　into　consideration,　especially　when　the　image　spatial　size　is　larger.　An　improved　band　similarity-based　band　selection　method　is　proposed　for　hyperspectral　imagery　target　detection,　which　includes　four　steps:　(1)　bad　bands　are　removed　by　data　preprocessing;　(2)　several　selected　pixels　are　used　for　band　selection　instead　of　using　all　the　pixels　to　reduce　the　computational　complexity;　(3)　hyperspectral　imagery　is　analyzed　for　target　detection;　and　(4)　the　number　of　selected　bands　is　determined　by　adjusting　the　threshold　of　similarity　metric,　to　ensure　target　detection　operators　have　the　best　performance　with　selected　bands.　In　the　example,　the　well-known　adaptive　coherence　estimator　detector　was　used　to　evaluate　the　effectiveness　of　the　proposed　band　selection　method.　The　receiver　operating　characteristics　curves　were　plotted　to　prove　the　proposed　algorithm　quantitatively.　The　experimental　results　show　that　our　method　can　yield　a　better　result　in　target　detection　than　other　band　selection　methods.　(C)　2015　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)